Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Swarm intelligence
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Improved binary PSO for feature selection using gene expression data
Computational Biology and Chemistry
Different metaheuristic strategies to solve the feature selection problem
Pattern Recognition Letters
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
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The combinatorial nature of the Feature Selection problem has made the use of heuristic methods indispensable even for moderate dataset dimensions. Recently, several optimization paradigms emerged as attractive alternatives to classic heuristic based approaches. In this paper, we propose a new an adapted Particle Swarm Optimization for the exploration of the feature selection problem search space. In spite of the combinatorial nature of the feature selection problem, the investigated approach is based on the original PSO formulation and integrates wrapper-filter methods within uniform framework. Empirical study compares and discusses the effectiveness of the devised methods on a set of featured benchmarks